Im, Sungjin

12 publications

NeurIPS 2025 A Beyond-Worst-Case Analysis of Greedy K-Means++ Qingyun Chen, Sungjin Im, Benjamin Moseley, Ryan Milstrey, Chenyang Xu, Ruilong Zhang

NeurIPS 2024 Binary Search with Distributional Predictions Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Aidin Niaparast, Sergei Vassilvitskii

AAAI 2024 Sampling for Beyond-Worst-Case Online Ranking Qingyun Chen, Sungjin Im, Benjamin Moseley, Chenyang Xu, Ruilong Zhang

AAAI 2023 Min-Max Submodular Ranking for Multiple Agents Qingyun Chen, Sungjin Im, Benjamin Moseley, Chenyang Xu, Ruilong Zhang

ECML-PKDD 2023 Online State Exploration: Competitive Worst Case and Learning-Augmented Algorithms Sungjin Im, Benjamin Moseley, Chenyang Xu, Ruilong Zhang

NeurIPS 2022 Algorithms with Prediction Portfolios Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii

ICML 2022 Parsimonious Learning-Augmented Caching Sungjin Im, Ravi Kumar, Aditya Petety, Manish Purohit

NeurIPS 2021 Faster Matchings via Learned Duals Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii

NeurIPS 2021 Online Knapsack with Frequency Predictions Sungjin Im, Ravi Kumar, Mahshid Montazer Qaem, Manish Purohit

AISTATS 2020 Fast Noise Removal for K-Means Clustering Sungjin Im, Mahshid Montazer Qaem, Benjamin Moseley, Xiaorui Sun, Rudy Zhou

AISTATS 2020 Unconditional Coresets for Regularized Loss Minimization Alireza Samadian, Kirk Pruhs, Benjamin Moseley, Sungjin Im, Ryan Curtin

ECML-PKDD 2019 Fast and Parallelizable Ranking with Outliers from Pairwise Comparisons Sungjin Im, Mahshid Montazer Qaem